title: Identification of incident injuries in hospital discharge registers.
abstract: BACKGROUND
  Hospital discharge data on injuries constitute a potentially powerful data source for epidemiologic studies. However, reliable identification of incident injury admissions is necessary. The objective of this study was to develop a prediction model for identifying incident hospital admissions, based on variables derived from a hospital discharge register.
  
  
  METHODS
  There were 743,022 hospital admissions for injury in Sweden 1998-2004. Of these, 23,920 were in the county of Uppsala and 24% of these people had previous injury admissions. To determine if these admissions were new injuries or readmissions for earlier injuries, we reviewed 817 randomly selected hospital records. A prediction model for incident injury admissions was developed on the basis of patient age, type of admission (urgent or elective), time interval from the previous injury admission, main diagnosis, and department type.
  
  
  RESULTS
  The final prediction model showed good discrimination (c-statistic = 0.969). This model was applied to the validation dataset using the optimal cut-off level, and the resulting sensitivity and specificity were adjusted according to the proportion with a previous injury admission in each injury category. The injury with the highest proportion of possible readmissions was hip contusion (35%). Nevertheless, using the prediction model, incident hip contusions were identified with a sensitivity of 94% (95% confidence interval = 93%-95%) and a specificity of 95% (94%-97%). The accuracy was higher for all other injury categories.
  
  
  CONCLUSIONS
  Incident injury admissions can be accurately separated from readmissions using a prediction model based on information derived from hospital discharge data.
authors.affiliation: Department of Surgical Sciences, Uppsala Clinical Research Center, Uppsala University Hospital, Uppsala, Sweden. rolf.gedeborg@surgsci.uu.se
authors.forename: Rolf
authors.initials: R
authors.lastname: Gedeborg
authors.forename: Henrik
authors.initials: H
authors.lastname: Engquist
authors.forename: Lars
authors.initials: L
authors.lastname: Berglund
authors.forename: Karl
authors.initials: K
authors.lastname: Michaelsson
mesh_terms: D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D015331:Cohort Studies; D005260:Female; D006801:Humans; D008297:Male; D008499:Medical Records / Q000706:statistics & numerical data*; D008875:Middle Aged; D015233:Models, Statistical; D010351:Patient Discharge / Q000706:statistics & numerical data*; D013548:Sweden / Q000453:epidemiology; D014947:Wounds and Injuries / Q000175:diagnosis / Q000453:epidemiology*; D055815:Young Adult